import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from sklearn.model_selection import GridSearchCV,RandomizedSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB,GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC,LinearSVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,BaggingClassifier
from sklearn.metrics import f1_score, classification_report, confusion_matrix,accuracy_score,log_loss,roc_auc_score,precision_score,recall_score
from sklearn.model_selection import KFold, cross_val_score
signal_df = pd.read_csv('signal-data.csv')
#Size of the dataset : 1567 rows and 592 columns
signal_df.shape
(1567, 592)
#Datatypes of the columns in dataset
signal_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1567 entries, 0 to 1566 Columns: 592 entries, Time to Pass/Fail dtypes: float64(590), int64(1), object(1) memory usage: 7.1+ MB
#5 point summary
signal_df.describe()
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 581 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1561.000000 | 1560.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1553.0 | 1553.000000 | 1558.000000 | 1565.000000 | 1565.000000 | ... | 618.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1567.000000 |
| mean | 3014.452896 | 2495.850231 | 2200.547318 | 1396.376627 | 4.197013 | 100.0 | 101.112908 | 0.121822 | 1.462862 | -0.000841 | ... | 97.934373 | 0.500096 | 0.015318 | 0.003847 | 3.067826 | 0.021458 | 0.016475 | 0.005283 | 99.670066 | -0.867262 |
| std | 73.621787 | 80.407705 | 29.513152 | 441.691640 | 56.355540 | 0.0 | 6.237214 | 0.008961 | 0.073897 | 0.015116 | ... | 87.520966 | 0.003404 | 0.017180 | 0.003720 | 3.578033 | 0.012358 | 0.008808 | 0.002867 | 93.891919 | 0.498010 |
| min | 2743.240000 | 2158.750000 | 2060.660000 | 0.000000 | 0.681500 | 100.0 | 82.131100 | 0.000000 | 1.191000 | -0.053400 | ... | 0.000000 | 0.477800 | 0.006000 | 0.001700 | 1.197500 | -0.016900 | 0.003200 | 0.001000 | 0.000000 | -1.000000 |
| 25% | 2966.260000 | 2452.247500 | 2181.044400 | 1081.875800 | 1.017700 | 100.0 | 97.920000 | 0.121100 | 1.411200 | -0.010800 | ... | 46.184900 | 0.497900 | 0.011600 | 0.003100 | 2.306500 | 0.013425 | 0.010600 | 0.003300 | 44.368600 | -1.000000 |
| 50% | 3011.490000 | 2499.405000 | 2201.066700 | 1285.214400 | 1.316800 | 100.0 | 101.512200 | 0.122400 | 1.461600 | -0.001300 | ... | 72.288900 | 0.500200 | 0.013800 | 0.003600 | 2.757650 | 0.020500 | 0.014800 | 0.004600 | 71.900500 | -1.000000 |
| 75% | 3056.650000 | 2538.822500 | 2218.055500 | 1591.223500 | 1.525700 | 100.0 | 104.586700 | 0.123800 | 1.516900 | 0.008400 | ... | 116.539150 | 0.502375 | 0.016500 | 0.004100 | 3.295175 | 0.027600 | 0.020300 | 0.006400 | 114.749700 | -1.000000 |
| max | 3356.350000 | 2846.440000 | 2315.266700 | 3715.041700 | 1114.536600 | 100.0 | 129.252200 | 0.128600 | 1.656400 | 0.074900 | ... | 737.304800 | 0.509800 | 0.476600 | 0.104500 | 99.303200 | 0.102800 | 0.079900 | 0.028600 | 737.304800 | 1.000000 |
8 rows × 591 columns
signal_df.head(5)
| Time | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ... | 581 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2008-07-19 11:55:00 | 3030.93 | 2564.00 | 2187.7333 | 1411.1265 | 1.3602 | 100.0 | 97.6133 | 0.1242 | 1.5005 | ... | NaN | 0.5005 | 0.0118 | 0.0035 | 2.3630 | NaN | NaN | NaN | NaN | -1 |
| 1 | 2008-07-19 12:32:00 | 3095.78 | 2465.14 | 2230.4222 | 1463.6606 | 0.8294 | 100.0 | 102.3433 | 0.1247 | 1.4966 | ... | 208.2045 | 0.5019 | 0.0223 | 0.0055 | 4.4447 | 0.0096 | 0.0201 | 0.0060 | 208.2045 | -1 |
| 2 | 2008-07-19 13:17:00 | 2932.61 | 2559.94 | 2186.4111 | 1698.0172 | 1.5102 | 100.0 | 95.4878 | 0.1241 | 1.4436 | ... | 82.8602 | 0.4958 | 0.0157 | 0.0039 | 3.1745 | 0.0584 | 0.0484 | 0.0148 | 82.8602 | 1 |
| 3 | 2008-07-19 14:43:00 | 2988.72 | 2479.90 | 2199.0333 | 909.7926 | 1.3204 | 100.0 | 104.2367 | 0.1217 | 1.4882 | ... | 73.8432 | 0.4990 | 0.0103 | 0.0025 | 2.0544 | 0.0202 | 0.0149 | 0.0044 | 73.8432 | -1 |
| 4 | 2008-07-19 15:22:00 | 3032.24 | 2502.87 | 2233.3667 | 1326.5200 | 1.5334 | 100.0 | 100.3967 | 0.1235 | 1.5031 | ... | NaN | 0.4800 | 0.4766 | 0.1045 | 99.3032 | 0.0202 | 0.0149 | 0.0044 | 73.8432 | -1 |
5 rows × 592 columns
#Check for null values
signal_df.isnull().sum()
Time 0
0 6
1 7
2 14
3 14
..
586 1
587 1
588 1
589 1
Pass/Fail 0
Length: 592, dtype: int64
#Dropping null values.
signal_processed_df=signal_df.dropna(axis='columns')
signal_processed_df.dropna()
| Time | 20 | 86 | 87 | 88 | 113 | 114 | 115 | 116 | 117 | ... | 527 | 570 | 571 | 572 | 573 | 574 | 575 | 576 | 577 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2008-07-19 11:55:00 | 1.4026 | 2.3895 | 0.9690 | 1747.6049 | 0.9460 | 0.0 | 748.6115 | 0.9908 | 58.4306 | ... | 6.6926 | 533.8500 | 2.1113 | 8.95 | 0.3157 | 3.0624 | 0.1026 | 1.6765 | 14.9509 | -1 |
| 1 | 2008-07-19 12:32:00 | 1.3825 | 2.3754 | 0.9894 | 1931.6464 | 0.9425 | 0.0 | 731.2517 | 0.9902 | 58.6680 | ... | 8.8370 | 535.0164 | 2.4335 | 5.92 | 0.2653 | 2.0111 | 0.0772 | 1.1065 | 10.9003 | -1 |
| 2 | 2008-07-19 13:17:00 | 1.4123 | 2.4532 | 0.9880 | 1685.8514 | 0.9231 | 0.0 | 718.5777 | 0.9899 | 58.4808 | ... | 6.4568 | 535.0245 | 2.0293 | 11.21 | 0.1882 | 4.0923 | 0.0640 | 2.0952 | 9.2721 | 1 |
| 3 | 2008-07-19 14:43:00 | 1.4011 | 2.4004 | 0.9904 | 1752.0968 | 0.9564 | 0.0 | 709.0867 | 0.9906 | 58.6635 | ... | 6.4865 | 530.5682 | 2.0253 | 9.33 | 0.1738 | 2.8971 | 0.0525 | 1.7585 | 8.5831 | -1 |
| 4 | 2008-07-19 15:22:00 | 1.3888 | 2.4530 | 0.9902 | 1828.3846 | 0.9424 | 0.0 | 796.5950 | 0.9908 | 58.3858 | ... | 6.3745 | 532.0155 | 2.0275 | 8.83 | 0.2224 | 3.1776 | 0.0706 | 1.6597 | 10.9698 | -1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1562 | 2008-10-16 15:13:00 | 1.4072 | 2.3762 | 0.9861 | 1869.4215 | 0.9520 | 0.0 | 727.6761 | 0.9894 | 58.3514 | ... | 2.6649 | 536.3418 | 2.0153 | 7.98 | 0.2363 | 2.6401 | 0.0785 | 1.4879 | 11.7256 | -1 |
| 1563 | 2008-10-16 20:49:00 | 1.3949 | 2.4880 | 0.9911 | 1872.5133 | 0.9561 | 0.0 | 755.7527 | 0.9899 | 57.1020 | ... | 6.0192 | 537.9264 | 2.1814 | 5.48 | 0.3891 | 1.9077 | 0.1213 | 1.0187 | 17.8379 | -1 |
| 1564 | 2008-10-17 05:26:00 | 1.4256 | 2.4590 | 0.9869 | 1820.3629 | 0.9488 | 0.0 | 704.2686 | 0.9891 | 59.2046 | ... | 5.4641 | 530.3709 | 2.3435 | 6.49 | 0.4154 | 2.1760 | 0.1352 | 1.2237 | 17.7267 | -1 |
| 1565 | 2008-10-17 06:01:00 | 1.3868 | 2.3600 | 0.9796 | 1627.4714 | 0.9485 | 0.0 | 605.6190 | 0.9896 | 58.2686 | ... | 6.5512 | 534.3936 | 1.9098 | 9.13 | 0.3669 | 3.2524 | 0.1040 | 1.7085 | 19.2104 | -1 |
| 1566 | 2008-10-17 06:07:00 | 1.4048 | 2.3701 | 0.9758 | 1759.9908 | 0.9432 | 0.0 | 683.5622 | 0.9893 | 59.8578 | ... | 4.1651 | 528.7918 | 2.0831 | 6.81 | 0.4774 | 2.2727 | 0.1495 | 1.2878 | 22.9183 | -1 |
1567 rows × 54 columns
#Check for null values after dropping null values
signal_processed_df.isnull().sum()
Time 0 20 0 86 0 87 0 88 0 113 0 114 0 115 0 116 0 117 0 119 0 120 0 156 0 221 0 222 0 223 0 248 0 249 0 250 0 251 0 252 0 254 0 255 0 291 0 359 0 360 0 361 0 386 0 387 0 388 0 389 0 390 0 392 0 393 0 429 0 493 0 494 0 495 0 520 0 521 0 522 0 523 0 524 0 526 0 527 0 570 0 571 0 572 0 573 0 574 0 575 0 576 0 577 0 Pass/Fail 0 dtype: int64
signal_processed_df.isnull().values.any()
False
#Check for columns that have few values
unique_count=signal_processed_df.nunique()
unique_count
Time 1534 20 552 86 472 87 249 88 973 113 468 114 20 115 1567 116 136 117 1527 119 249 120 1269 156 722 221 408 222 178 223 973 248 426 249 23 250 1566 251 143 252 1533 254 291 255 1317 291 354 359 215 360 97 361 972 386 191 387 22 388 1564 389 92 390 1458 392 109 393 953 429 1542 493 572 494 894 495 964 520 1536 521 9 522 1562 523 1040 524 1543 526 1514 527 1549 570 814 571 811 572 518 573 754 574 830 575 599 576 820 577 851 Pass/Fail 2 dtype: int64
#Drop the columns that have single values
to_del = [i for i,v in enumerate(unique_count) if v == 1]
to_del
[]
signal_processed_df.drop(columns=to_del,axis=1)
| Time | 20 | 86 | 87 | 88 | 113 | 114 | 115 | 116 | 117 | ... | 527 | 570 | 571 | 572 | 573 | 574 | 575 | 576 | 577 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2008-07-19 11:55:00 | 1.4026 | 2.3895 | 0.9690 | 1747.6049 | 0.9460 | 0.0 | 748.6115 | 0.9908 | 58.4306 | ... | 6.6926 | 533.8500 | 2.1113 | 8.95 | 0.3157 | 3.0624 | 0.1026 | 1.6765 | 14.9509 | -1 |
| 1 | 2008-07-19 12:32:00 | 1.3825 | 2.3754 | 0.9894 | 1931.6464 | 0.9425 | 0.0 | 731.2517 | 0.9902 | 58.6680 | ... | 8.8370 | 535.0164 | 2.4335 | 5.92 | 0.2653 | 2.0111 | 0.0772 | 1.1065 | 10.9003 | -1 |
| 2 | 2008-07-19 13:17:00 | 1.4123 | 2.4532 | 0.9880 | 1685.8514 | 0.9231 | 0.0 | 718.5777 | 0.9899 | 58.4808 | ... | 6.4568 | 535.0245 | 2.0293 | 11.21 | 0.1882 | 4.0923 | 0.0640 | 2.0952 | 9.2721 | 1 |
| 3 | 2008-07-19 14:43:00 | 1.4011 | 2.4004 | 0.9904 | 1752.0968 | 0.9564 | 0.0 | 709.0867 | 0.9906 | 58.6635 | ... | 6.4865 | 530.5682 | 2.0253 | 9.33 | 0.1738 | 2.8971 | 0.0525 | 1.7585 | 8.5831 | -1 |
| 4 | 2008-07-19 15:22:00 | 1.3888 | 2.4530 | 0.9902 | 1828.3846 | 0.9424 | 0.0 | 796.5950 | 0.9908 | 58.3858 | ... | 6.3745 | 532.0155 | 2.0275 | 8.83 | 0.2224 | 3.1776 | 0.0706 | 1.6597 | 10.9698 | -1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1562 | 2008-10-16 15:13:00 | 1.4072 | 2.3762 | 0.9861 | 1869.4215 | 0.9520 | 0.0 | 727.6761 | 0.9894 | 58.3514 | ... | 2.6649 | 536.3418 | 2.0153 | 7.98 | 0.2363 | 2.6401 | 0.0785 | 1.4879 | 11.7256 | -1 |
| 1563 | 2008-10-16 20:49:00 | 1.3949 | 2.4880 | 0.9911 | 1872.5133 | 0.9561 | 0.0 | 755.7527 | 0.9899 | 57.1020 | ... | 6.0192 | 537.9264 | 2.1814 | 5.48 | 0.3891 | 1.9077 | 0.1213 | 1.0187 | 17.8379 | -1 |
| 1564 | 2008-10-17 05:26:00 | 1.4256 | 2.4590 | 0.9869 | 1820.3629 | 0.9488 | 0.0 | 704.2686 | 0.9891 | 59.2046 | ... | 5.4641 | 530.3709 | 2.3435 | 6.49 | 0.4154 | 2.1760 | 0.1352 | 1.2237 | 17.7267 | -1 |
| 1565 | 2008-10-17 06:01:00 | 1.3868 | 2.3600 | 0.9796 | 1627.4714 | 0.9485 | 0.0 | 605.6190 | 0.9896 | 58.2686 | ... | 6.5512 | 534.3936 | 1.9098 | 9.13 | 0.3669 | 3.2524 | 0.1040 | 1.7085 | 19.2104 | -1 |
| 1566 | 2008-10-17 06:07:00 | 1.4048 | 2.3701 | 0.9758 | 1759.9908 | 0.9432 | 0.0 | 683.5622 | 0.9893 | 59.8578 | ... | 4.1651 | 528.7918 | 2.0831 | 6.81 | 0.4774 | 2.2727 | 0.1495 | 1.2878 | 22.9183 | -1 |
1567 rows × 54 columns
signal_processed_df.drop(to_del, axis=1)
print(signal_processed_df.shape)
(1567, 54)
signal_processed_df.drop('Time',axis=1,inplace=True)
C:\Users\AshwiniShivaprasad\anaconda\lib\site-packages\pandas\core\frame.py:3997: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy errors=errors,
#Correlation between variables
signal_corr = signal_processed_df.corr()
signal_corr
| 20 | 86 | 87 | 88 | 113 | 114 | 115 | 116 | 117 | 119 | ... | 527 | 570 | 571 | 572 | 573 | 574 | 575 | 576 | 577 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | 1.000000 | 0.050032 | 0.055319 | -0.065107 | 0.024664 | -0.031431 | 0.025619 | 0.046091 | 0.019993 | -0.001825 | ... | -0.046716 | -0.000205 | -0.021436 | -0.025429 | -0.020366 | -0.022118 | -0.025880 | -0.024596 | -0.026571 | 0.023253 |
| 86 | 0.050032 | 1.000000 | 0.031000 | -0.003571 | 0.014488 | -0.024863 | -0.014257 | 0.028356 | 0.014794 | -0.001545 | ... | -0.043544 | -0.006474 | -0.012701 | 0.033938 | 0.052387 | 0.029529 | 0.056517 | 0.032696 | 0.053237 | 0.024974 |
| 87 | 0.055319 | 0.031000 | 1.000000 | -0.061580 | 0.106847 | -0.042029 | -0.052125 | -0.003568 | -0.033015 | -0.005589 | ... | -0.026713 | 0.025988 | -0.031415 | 0.012448 | 0.028469 | 0.007819 | 0.027971 | 0.009253 | 0.035851 | -0.030422 |
| 88 | -0.065107 | -0.003571 | -0.061580 | 1.000000 | 0.100660 | 0.009750 | 0.116503 | 0.003269 | 0.010875 | 0.026516 | ... | -0.003777 | -0.022459 | 0.020736 | -0.026952 | -0.017848 | -0.026170 | -0.016366 | -0.024665 | -0.021203 | 0.026865 |
| 113 | 0.024664 | 0.014488 | 0.106847 | 0.100660 | 1.000000 | -0.285839 | -0.041832 | 0.056295 | 0.019973 | 0.025750 | ... | -0.047714 | 0.068486 | -0.135673 | 0.000455 | -0.019055 | -0.006844 | -0.033629 | -0.005915 | 0.010921 | 0.001328 |
| 114 | -0.031431 | -0.024863 | -0.042029 | 0.009750 | -0.285839 | 1.000000 | -0.005372 | -0.046802 | -0.007135 | -0.034011 | ... | 0.005271 | 0.009354 | 0.048395 | -0.013727 | -0.006772 | -0.013679 | 0.003335 | -0.013976 | -0.013607 | 0.068655 |
| 115 | 0.025619 | -0.014257 | -0.052125 | 0.116503 | -0.041832 | -0.005372 | 1.000000 | 0.000273 | -0.005960 | 0.042306 | ... | 0.001207 | -0.019066 | -0.024488 | 0.030013 | 0.036066 | 0.027323 | 0.040390 | 0.030523 | 0.041202 | -0.043654 |
| 116 | 0.046091 | 0.028356 | -0.003568 | 0.003269 | 0.056295 | -0.046802 | 0.000273 | 1.000000 | -0.070123 | -0.010319 | ... | 0.058791 | 0.006897 | -0.096214 | 0.024899 | -0.016263 | 0.024734 | -0.010626 | 0.023093 | 0.005018 | -0.012912 |
| 117 | 0.019993 | 0.014794 | -0.033015 | 0.010875 | 0.019973 | -0.007135 | -0.005960 | -0.070123 | 1.000000 | -0.012869 | ... | -0.033664 | -0.003324 | -0.017164 | -0.002197 | -0.009813 | -0.002848 | -0.009048 | -0.002175 | -0.006304 | -0.016720 |
| 119 | -0.001825 | -0.001545 | -0.005589 | 0.026516 | 0.025750 | -0.034011 | 0.042306 | -0.010319 | -0.012869 | 1.000000 | ... | 0.073035 | -0.004159 | -0.031574 | -0.013043 | -0.016697 | -0.014020 | -0.012939 | -0.013427 | -0.009130 | 0.005612 |
| 120 | -0.025870 | -0.025436 | 0.018598 | -0.029887 | -0.064379 | 0.026087 | -0.020330 | -0.033373 | -0.006748 | -0.314995 | ... | 0.095576 | -0.008374 | 0.017703 | 0.021675 | -0.004254 | 0.023976 | -0.008324 | 0.024473 | -0.009210 | -0.020277 |
| 156 | -0.480092 | -0.024185 | 0.000587 | 0.015746 | 0.007298 | 0.003831 | -0.009939 | -0.014849 | 0.011803 | 0.028717 | ... | 0.050375 | -0.001302 | 0.019842 | 0.005135 | 0.043805 | 0.004994 | 0.059437 | 0.004463 | 0.030379 | 0.002154 |
| 221 | 0.035726 | -0.094688 | 0.013984 | -0.048973 | -0.050905 | 0.034508 | -0.001386 | -0.049050 | 0.006011 | 0.003222 | ... | -0.038337 | -0.018657 | 0.022123 | 0.053900 | 0.046349 | 0.055708 | 0.048524 | 0.055600 | 0.038010 | 0.021609 |
| 222 | -0.036882 | -0.029315 | -0.666112 | -0.012177 | -0.056095 | 0.009713 | 0.056708 | 0.009592 | 0.001133 | 0.010617 | ... | 0.007053 | -0.035865 | 0.024664 | -0.002391 | -0.014555 | 0.002912 | -0.009121 | 0.001792 | -0.019954 | 0.031294 |
| 223 | 0.035709 | 0.033215 | -0.184025 | -0.063731 | -0.018882 | -0.016422 | -0.024287 | 0.001550 | 0.004167 | -0.002402 | ... | -0.056952 | 0.000965 | -0.011732 | 0.002504 | -0.001675 | 0.002391 | 0.001668 | 0.001301 | 0.004871 | -0.001068 |
| 248 | -0.028265 | -0.009478 | -0.027136 | 0.022275 | -0.462054 | 0.701354 | -0.016583 | -0.030914 | -0.003855 | -0.033057 | ... | 0.001519 | -0.026059 | 0.040836 | -0.004445 | -0.002235 | -0.001659 | 0.008389 | -0.002144 | -0.009722 | 0.008912 |
| 249 | -0.033351 | -0.028947 | -0.036359 | 0.021289 | -0.295515 | 0.977050 | -0.011662 | -0.052847 | -0.008050 | -0.032172 | ... | -0.004823 | 0.009649 | 0.042595 | -0.013239 | -0.005529 | -0.013191 | 0.004067 | -0.013469 | -0.011710 | 0.066478 |
| 250 | -0.001040 | -0.001482 | 0.017542 | 0.084469 | -0.015657 | 0.024117 | 0.020353 | 0.033181 | 0.026008 | 0.024171 | ... | -0.053820 | -0.016350 | -0.034609 | 0.017044 | -0.004188 | 0.015737 | -0.002322 | 0.016874 | 0.004559 | 0.006964 |
| 251 | -0.008267 | -0.011079 | 0.008984 | 0.014247 | 0.004538 | -0.005007 | 0.010882 | -0.500774 | 0.208033 | 0.042319 | ... | -0.049977 | 0.009655 | 0.018542 | -0.015878 | -0.009009 | -0.016023 | -0.007426 | -0.015982 | -0.013056 | -0.010315 |
| 252 | 0.020560 | 0.012529 | -0.033380 | 0.014709 | 0.016530 | -0.001406 | -0.003153 | -0.150705 | 0.986193 | -0.013886 | ... | -0.037093 | 0.001676 | -0.011364 | -0.005791 | -0.009891 | -0.005905 | -0.009395 | -0.005894 | -0.008353 | -0.006634 |
| 254 | 0.004270 | -0.016767 | 0.002600 | -0.020535 | -0.042144 | 0.044471 | -0.037003 | 0.021205 | 0.002270 | -0.795787 | ... | 0.120521 | -0.004231 | 0.066721 | 0.026515 | 0.024411 | 0.028117 | 0.025388 | 0.028107 | 0.013087 | -0.021509 |
| 255 | -0.047036 | -0.044394 | -0.025703 | -0.006563 | -0.051747 | 0.006875 | 0.000931 | 0.056109 | -0.033801 | 0.053133 | ... | 0.997831 | -0.001087 | 0.110017 | 0.022886 | 0.002827 | 0.028420 | 0.022094 | 0.026680 | -0.017745 | -0.011730 |
| 291 | -0.486590 | -0.023837 | 0.002259 | 0.012306 | 0.004996 | 0.003199 | -0.012179 | -0.014742 | 0.009742 | 0.021806 | ... | 0.046912 | 0.000080 | 0.021581 | 0.005496 | 0.043071 | 0.005266 | 0.058470 | 0.004658 | 0.029990 | 0.000007 |
| 359 | 0.040344 | -0.090202 | 0.022785 | -0.050066 | -0.042738 | 0.036278 | 0.004214 | -0.048648 | 0.008959 | 0.007212 | ... | -0.055379 | -0.025933 | 0.019865 | 0.054428 | 0.055168 | 0.056947 | 0.056926 | 0.056607 | 0.046606 | 0.033077 |
| 360 | -0.037356 | -0.030128 | -0.690430 | -0.010635 | -0.057063 | 0.009287 | 0.063079 | 0.010452 | 0.001306 | 0.011922 | ... | 0.008175 | -0.032558 | 0.018659 | -0.003721 | -0.016447 | 0.001051 | -0.011477 | 0.000014 | -0.020803 | 0.038608 |
| 361 | 0.043506 | 0.034337 | -0.186550 | -0.037648 | -0.015730 | -0.006272 | -0.014255 | 0.003708 | -0.000094 | -0.008515 | ... | -0.047953 | -0.001907 | -0.014840 | 0.011358 | 0.005154 | 0.011294 | 0.010484 | 0.010288 | 0.012923 | -0.004971 |
| 386 | -0.028633 | -0.007842 | -0.026420 | 0.020615 | -0.461221 | 0.697562 | -0.021760 | -0.028299 | -0.003257 | -0.030943 | ... | -0.002350 | -0.022088 | 0.036144 | -0.004357 | -0.002952 | -0.001635 | 0.007510 | -0.002460 | -0.009458 | 0.008212 |
| 387 | -0.033328 | -0.028726 | -0.036401 | 0.020506 | -0.295545 | 0.978660 | -0.011260 | -0.052732 | -0.008047 | -0.032175 | ... | -0.004338 | 0.009652 | 0.042901 | -0.013259 | -0.005653 | -0.013209 | 0.003982 | -0.013490 | -0.011864 | 0.066315 |
| 388 | -0.006756 | 0.012244 | 0.015236 | 0.094324 | -0.025518 | 0.036863 | 0.006647 | 0.028367 | 0.033001 | 0.027856 | ... | -0.059447 | -0.014040 | -0.029975 | 0.013263 | -0.003690 | 0.013122 | -0.000915 | 0.012834 | 0.004772 | 0.019723 |
| 389 | -0.008013 | -0.010957 | 0.009095 | 0.014363 | 0.004956 | -0.004716 | 0.010860 | -0.495624 | 0.207895 | 0.042254 | ... | -0.050032 | 0.009728 | 0.017785 | -0.015466 | -0.008797 | -0.015609 | -0.007202 | -0.015580 | -0.012691 | -0.010372 |
| 390 | 0.020230 | 0.012624 | -0.033452 | 0.014787 | 0.016410 | -0.001608 | -0.003359 | -0.150415 | 0.986234 | -0.014036 | ... | -0.037297 | 0.001687 | -0.011406 | -0.005937 | -0.010102 | -0.006054 | -0.009604 | -0.006046 | -0.008538 | -0.006805 |
| 392 | 0.011899 | -0.021890 | 0.012117 | -0.027125 | -0.049537 | 0.044315 | -0.040157 | 0.022692 | 0.005072 | -0.786370 | ... | 0.119740 | -0.002722 | 0.068766 | 0.027564 | 0.027336 | 0.029321 | 0.028653 | 0.029100 | 0.014539 | -0.021268 |
| 393 | -0.045068 | -0.041500 | -0.028994 | -0.005397 | -0.062275 | 0.019206 | -0.002040 | 0.058764 | -0.035212 | 0.022361 | ... | 0.981828 | 0.003653 | 0.124244 | 0.022105 | 0.004639 | 0.026793 | 0.024246 | 0.025619 | -0.018353 | -0.003346 |
| 429 | -0.490262 | -0.024608 | 0.000886 | 0.014120 | 0.005998 | 0.003269 | -0.007623 | -0.014416 | 0.009915 | 0.027705 | ... | 0.051199 | -0.000686 | 0.024671 | 0.004021 | 0.046718 | 0.003838 | 0.063353 | 0.003398 | 0.031586 | 0.000275 |
| 493 | 0.033054 | -0.138023 | 0.012088 | -0.048142 | -0.051803 | 0.036174 | -0.001609 | -0.049814 | 0.005315 | 0.002799 | ... | -0.035344 | -0.017544 | 0.023735 | 0.051172 | 0.043311 | 0.053146 | 0.045353 | 0.052911 | 0.034694 | 0.019420 |
| 494 | -0.036874 | -0.029911 | -0.677319 | -0.013591 | -0.054756 | 0.007912 | 0.060111 | 0.010613 | 0.001017 | 0.011676 | ... | 0.007017 | -0.032042 | 0.020355 | -0.003361 | -0.015688 | 0.001396 | -0.010575 | 0.000344 | -0.020297 | 0.035182 |
| 495 | 0.037812 | 0.031144 | -0.182408 | -0.129776 | -0.024838 | -0.016103 | -0.034123 | 0.002018 | 0.003589 | -0.004871 | ... | -0.053074 | 0.002131 | -0.012245 | 0.004173 | 0.000152 | 0.004011 | 0.003778 | 0.002885 | 0.006519 | -0.003532 |
| 520 | -0.028068 | -0.009157 | -0.025481 | 0.023494 | -0.459774 | 0.698559 | -0.012841 | -0.030124 | -0.004070 | -0.033609 | ... | -0.000060 | -0.024243 | 0.038910 | -0.004874 | -0.002480 | -0.002217 | 0.007880 | -0.002699 | -0.009651 | 0.007691 |
| 521 | -0.036054 | -0.040938 | 0.003335 | -0.021524 | -0.261069 | 0.606741 | 0.020500 | -0.021769 | -0.008197 | -0.030059 | ... | 0.018433 | 0.004552 | 0.046446 | -0.004062 | 0.004157 | -0.003878 | 0.014788 | -0.004547 | -0.002178 | 0.036722 |
| 522 | -0.007499 | -0.000712 | 0.019417 | 0.069434 | 0.002334 | 0.025102 | -0.123783 | 0.032780 | 0.027453 | 0.020292 | ... | -0.053266 | -0.014240 | -0.033600 | 0.013424 | -0.008386 | 0.012473 | -0.006858 | 0.013245 | -0.000141 | 0.013677 |
| 523 | -0.008447 | -0.011536 | 0.009296 | 0.013958 | 0.004581 | -0.004909 | 0.010731 | -0.495068 | 0.201033 | 0.042226 | ... | -0.049975 | 0.009665 | 0.018676 | -0.015536 | -0.008837 | -0.015673 | -0.007190 | -0.015640 | -0.012852 | -0.010401 |
| 524 | 0.019522 | 0.009908 | -0.032697 | 0.017311 | 0.014159 | 0.000792 | -0.001246 | -0.187591 | 0.978643 | -0.011759 | ... | -0.043273 | 0.003212 | -0.004323 | -0.007136 | -0.009400 | -0.007340 | -0.009941 | -0.007283 | -0.009246 | -0.005837 |
| 526 | 0.003283 | -0.015226 | 0.001353 | -0.019670 | -0.040447 | 0.043962 | -0.036525 | 0.021294 | 0.002296 | -0.813955 | ... | 0.118955 | -0.003672 | 0.065617 | 0.026038 | 0.023694 | 0.027615 | 0.024399 | 0.027608 | 0.012581 | -0.021536 |
| 527 | -0.046716 | -0.043544 | -0.026713 | -0.003777 | -0.047714 | 0.005271 | 0.001207 | 0.058791 | -0.033664 | 0.073035 | ... | 1.000000 | 0.000036 | 0.111009 | 0.021502 | 0.003734 | 0.026855 | 0.023573 | 0.025069 | -0.016891 | -0.010425 |
| 570 | -0.000205 | -0.006474 | 0.025988 | -0.022459 | 0.068486 | 0.009354 | -0.019066 | 0.006897 | -0.003324 | -0.004159 | ... | 0.000036 | 1.000000 | -0.095755 | -0.274167 | -0.288580 | -0.307529 | -0.316960 | -0.360498 | -0.247655 | -0.001656 |
| 571 | -0.021436 | -0.012701 | -0.031415 | 0.020736 | -0.135673 | 0.048395 | -0.024488 | -0.096214 | -0.017164 | -0.031574 | ... | 0.111009 | -0.095755 | 1.000000 | -0.151217 | 0.096217 | -0.138441 | 0.133902 | -0.136232 | -0.121115 | -0.019353 |
| 572 | -0.025429 | 0.033938 | 0.012448 | -0.026952 | 0.000455 | -0.013727 | 0.030013 | 0.024899 | -0.002197 | -0.013043 | ... | 0.021502 | -0.274167 | -0.151217 | 1.000000 | 0.787710 | 0.993689 | 0.775835 | 0.994772 | 0.863768 | -0.032233 |
| 573 | -0.020366 | 0.052387 | 0.028469 | -0.017848 | -0.019055 | -0.006772 | 0.036066 | -0.016263 | -0.009813 | -0.016697 | ... | 0.003734 | -0.288580 | 0.096217 | 0.787710 | 1.000000 | 0.781319 | 0.980265 | 0.790026 | 0.957874 | -0.051873 |
| 574 | -0.022118 | 0.029529 | 0.007819 | -0.026170 | -0.006844 | -0.013679 | 0.027323 | 0.024734 | -0.002848 | -0.014020 | ... | 0.026855 | -0.307529 | -0.138441 | 0.993689 | 0.781319 | 1.000000 | 0.774716 | 0.991738 | 0.851784 | -0.034713 |
| 575 | -0.025880 | 0.056517 | 0.027971 | -0.016366 | -0.033629 | 0.003335 | 0.040390 | -0.010626 | -0.009048 | -0.012939 | ... | 0.023573 | -0.316960 | 0.133902 | 0.775835 | 0.980265 | 0.774716 | 1.000000 | 0.780840 | 0.928311 | -0.052731 |
| 576 | -0.024596 | 0.032696 | 0.009253 | -0.024665 | -0.005915 | -0.013976 | 0.030523 | 0.023093 | -0.002175 | -0.013427 | ... | 0.025069 | -0.360498 | -0.136232 | 0.994772 | 0.790026 | 0.991738 | 0.780840 | 1.000000 | 0.859278 | -0.028488 |
| 577 | -0.026571 | 0.053237 | 0.035851 | -0.021203 | 0.010921 | -0.013607 | 0.041202 | 0.005018 | -0.006304 | -0.009130 | ... | -0.016891 | -0.247655 | -0.121115 | 0.863768 | 0.957874 | 0.851784 | 0.928311 | 0.859278 | 1.000000 | -0.049633 |
| Pass/Fail | 0.023253 | 0.024974 | -0.030422 | 0.026865 | 0.001328 | 0.068655 | -0.043654 | -0.012912 | -0.016720 | 0.005612 | ... | -0.010425 | -0.001656 | -0.019353 | -0.032233 | -0.051873 | -0.034713 | -0.052731 | -0.028488 | -0.049633 | 1.000000 |
53 rows × 53 columns
#heatmap
sns.heatmap(signal_corr,annot=True)
<matplotlib.axes._subplots.AxesSubplot at 0x2156597ac48>
#Distribution of variables
signal_processed_df.hist(stacked=False, bins=100, figsize=(12,30));
#Pairplot
sns.pairplot(signal_processed_df)
<seaborn.axisgrid.PairGrid at 0x21568b94f88>